In July, OpenAI revealed that one of its experimental AI agents attacked publicly accessible services, including the AI hosting platform Hugging Face, during internal security testing. Then, Anthropic disclosed that Claude had independently chained together exploits against real software and developed new techniques for finding weaknesses in code. Shortly afterward, Meta confirmed that one of its own AI models breached another organization's systems during an evaluation after a misconfiguration gave it internet access.
None of these incidents involved an AI model deciding on its own to attack random targets. Instead, researchers gave the models realistic tools, internet access, or vulnerable systems to see how well they could perform offensive cybersecurity tasks. What surprised many experts wasn't that the models tried to hack systems but how capable they proved to be once given the opportunity.
At the same time, AI companies have become much more willing to test those capabilities and reveal the results. Rather than keeping security evaluations behind closed doors, firms including OpenAI, Anthropic and Meta are publishing reports describing what happened when their newest systems were challenged by professional "red teams" — security experts tasked with deliberately finding weaknesses or ways to misuse a system.
Antonino Vaccaro, professor of business ethics at IESE Business School and director of its Observatory for AI Ethics in Organizations, agrees both factors are contributing to the recent spate of high-profile hacking stories.
Can AI really hack computers by itself?
Not exactly. Many headlines have described AI "escaping" test environments or acting autonomously. But experts said those descriptions can easily give the wrong impression.
Agha noted that these AI models are simply working to achieve a set objective. "The public should view these incidents as software optimization gone wrong, not as the dawn of a malicious, self-aware AI," he said. "It's less 'Terminator' and more like a very capable, literal-minded intern who breaks the law to finish a spreadsheet faster."
Dray Agha, senior manager of security operations at Huntress
The concern isn't that AI has become self-aware. It's that these systems are becoming increasingly effective at carrying out complicated technical tasks when given the right permissions.
"The game-changer is the shift from conversational models to agentic models," Agha said. "Today's frontier AI doesn't just answer questions. It can autonomously chain together actions, write code, use command-line tools, and iterate on its own failures."
The same capabilities aren't limited to attackers. Security teams are already using AI to review code for bugs, analyze suspicious files, and speed up investigations that would otherwise take analysts hours.
Should people be worried about AI committing cyberattacks?
The most immediate risk isn't AI deciding to launch attacks on its own, but cybercriminals using AI to commit familiar cybercrimes much faster than before.
"The threat is human malice, supercharged by AI scale and speed, not autonomous AI deciding to go rogue," Agha said.
How will AI change cyberattacks in the future?
The recent disclosures are unlikely to be the last. As AI companies race to build more capable systems, they are also giving those systems access to more tools, more computing resources and more realistic testing environments. That makes future evaluations more likely to uncover new — and occasionally alarming — behaviors.
Related storiesMost experts expect AI to become an increasingly powerful cybersecurity assistant rather than an independent cybercriminal. It will probably find software bugs faster, help defenders respond to attacks more quickly, and automate many routine security tasks. At the same time, criminals will use the same technology to improve phishing campaigns, accelerate vulnerability research and make attacks more convincing.
The next wave of AI security headlines is unlikely to be about machines plotting against humanity: It will instead likely be about increasingly capable software doing exactly what it has been asked to do — and showing just how much that capability has grown.
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